Question #90
Reading: Reading 1 Multiple Regression
PDF File: Reading 1 Multiple Regression.pdf
Page: 42
Status: Unattempted
Correct Answer: A
Question
Henry Hilton, CFA, is undertaking an analysis of the bicycle industry. He hypothesizes that bicycle sales (SALES) are a function of three factors: the population under 20 (POP), the level of disposable income (INCOME), and the number of dollars spent on advertising (ADV). All data are measured in millions of units. Hilton gathers data for the last 20 years. Which of the follow regression equations correctly represents Hilton's hypothesis?
Answer Choices:
A. SALES = α x β1 POP x β2 INCOME x β3 ADV x ε
B. INCOME = α + β1 POP + β2 SALES + β3 ADV + ε
C. SALES = α + β1 POP + β2 INCOME + β3 ADV + ε. Jessica Jenkins, CFA, is looking at the retail property sector for her manager. She is undertaking a top down review as she feels this is the best way to analyze the industry segment. To predict U.S. property starts (housing), she has used regression analysis. Jessica included the following variables in her analysis: Average nominal interest rates during each year (as a decimal) Annual GDP per capita in $'000 Given these variables, the following output was generated from 30 years of data: Exhibit 1 – Results from regressing housing starts (in millions) on interest rates and GDP per capita Coefficient Standard Error T-statistic
Explanation
SALES is the dependent variable. POP, INCOME, and ADV should be the independent
variables (on the right hand side) of the equation (in any order). Regression equations are
additive.
(Module 1.1, LOS 1.b)
Jessica Jenkins, CFA, is looking at the retail property sector for her manager. She is
undertaking a top down review as she feels this is the best way to analyze the industry
segment. To predict U.S. property starts (housing), she has used regression analysis.
Jessica included the following variables in her analysis:
Average nominal interest rates during each year (as a decimal)
Annual GDP per capita in $'000
Given these variables, the following output was generated from 30 years of data:
Exhibit 1 – Results from regressing housing starts (in millions) on interest rates and
GDP per capita
Coefficient
Standard Error
T-statistic
Intercept
Interest rate
0.42
–1.0
3.1
–2.0
GDP per capita
0.03
0.7
ANOVA
df
SS
MSS
F
Regression
2
3.896
1.948
21.644
Residual
27
2.431
0.090
Total
29
6.327
Observations
30
Durbin-Watson
1.27
Exhibit 2 - Critical Values for F-Distribution at 5% Level of Significance
Degrees of Freedom for the
Denominator
Degrees of Freedom (df) for the
Numerator
1
2
3
26
4.23
3.37
2.98
27
4.21
3.35
2.96
28
4.20
3.34
2.95
29
4.18
3.33
2.93
30
4.17
3.32
2.92
31
4.16
3.31
2.91
32
4.15
3.30
2.90
The following variable estimates have been made for 20X7:
GDP per capita = $46,700
Interest rate = 7%